Research map / Video / Video segmentation and tracking
Real-Time Neural Style Transfer for Videos
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Train the stylizer with a temporal consistency loss rather than applying image style transfer independently to frames | Feed-forward video style transfer · Sintel videos | temporal error: 0.0770 -> 0.0439; 0.0926 -> 0.0675; 0.0517 -> 0.0304; 0.0789 -> 0.0553; 0.0872 -> 0.0513 | Helped |
| Use feed-forward stylization instead of per-frame optimization | Video style transfer · Video frames | temporal error: 0.0439 vs 0.0252; 0.0675 vs 0.0512; 0.0304 vs 0.0195; 0.0553 vs 0.0407; 0.0513 vs 0.0361 | Hurt |
| Add non-consecutive frame pairs during training (ablation) | Ours · Sintel videos | temporal error: 0.0250 -> 0.0241; 0.0406 -> 0.0394; 0.0182 -> 0.0180; 0.0330 -> 0.0311; 0.0297 -> 0.0283 | Helped |
| Increase residual-block feature maps from 48 to 128 (ablation) | Ours · Sintel videos | temporal error: 0.0244 -> 0.0243; 0.0425 -> 0.0408; 0.0195 -> 0.0193; 0.0334 -> 0.0330; 0.0302 -> 0.0296 | Mixed |
| Reduce residual-block feature maps from 128 to 48 (ablation) | Ours · 1024 × 436 frame inference | inference time: 0.098 seconds -> 0.041 seconds | Helped |
Closest papers by meaning
- Consistent Video Style Transfer via Compound Regularization (AAAI 2020 · similarity 0.91)
- A Flexible Convolutional Solver for Fast Style Transfers (CVPR 2019 · similarity 0.91)
- Learning Linear Transformations for Fast Image and Video Style Transfer (CVPR 2019 · similarity 0.91)
- Adjustable Real-time Style Transfer (ICLR 2020 · similarity 0.90)
- Artistic Style Transfer with Internal-external Learning and Contrastive Learning (NeurIPS 2021 · similarity 0.90)
- Puff-Net: Efficient Style Transfer with Pure Content and Style Feature Fusion Network (CVPR 2024 · similarity 0.87)
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